Hadley Centre Technical Note 96 ... · Hadley Centre Technical Note 96 DisaggregationofdailydatainJULES ... and Douglas Clark, Centre for Ecology and Hydrology. ... This report documents

Hadley Centre Technical Note 96Disaggregation of daily data in JULES

July 23, 2014

Karina Williams, Met Office, and Douglas Clark, Centre for Ecologyand Hydrology

1 Introduction

The Joint UK Land Environment System (JULES) [Best et al., 2011, Clark et al., 2011] is a com-

munity land surface model that can be used both online as part of the Met Office Unified Modelling

system and offline for impacts studies. It has been designed to run at short time scales, in order to

exchange heat, momentum and moisture fluxes with an atmospheric model at model timesteps.

When run in offline mode, the meteorological data used to drive JULES is read from input files.

This forcing data is often only available at a temporal resolution of three hours or coarser, due

to the practical considerations in storing such a large amount of atmospheric model data and the

difficulties of obtaining observational data at this resolution. JULES has been shown to be sensitive

to the temporal resolution of the forcing data (e.g. Compton and Best [2011]).

In some cases, particularly for projects involving model intercomparisons (e.g. the Inter-Sectoral

Impact Model Inter- comparison Project (ISI-MIP) [Warszawski et al., 2013] and EUPORIAS [Hewitt

et al., 2013]), forcing data is only available at daily resolution. The absence of a diurnal cycle

for temperature and radiation and any sort of sub-daily structure for precipitation makes this data

completely unsuitable for forcing JULES directly. It is therefore necessary to perform some sort

of data disaggregation. To date, two main approaches have been used to disaggregate daily data

into sub-daily data for JULES: the WATCH [Weedon et al., 2011] approach, developed as part of

the EU-funded Water and Global Change programme, and the IMOGEN approach, used in the

IMOGEN modelling system [Huntingford et al., 2010] and the JULES runs for the ISI-MIP project

[Davie et al., 2013, Dankers et al., 2013]. The WATCH project published standalone fortran routines

to perform the disaggregation, whereas, in IMOGEN, the disaggregation is performed internally,

as the IMOGEN configuration of JULES is running. These approaches differ significantly in their

treatment of precipitation, with the WATCH disaggregator using a multiplicative cascade model to

distribute the precipitation and the IMOGEN approach modelling the days precipitation as arising

from one event of constant intensity and globally specified duration.

In JULES 4.0, the IMOGEN approach to disaggregation is made available in the JULES trunk

for standard (i.e. non-IMOGEN) runs. This report documents the method used and illustrates the

dependence on the value of the convective rain duration parameter chosen by the user, using a

development branch based on adding the disaggregation code to a branch of JULES 3.4.1, which

has been merged with the JULES trunk.

2 Disaggregation method

In JULES 4.0, disaggregation of daily forcing data to JULES model timesteps (typically of the order

of 30 minutes) can be switched on using the l_daily_disagg flag (see the JULES 4.0 manual for

more details of the user interface). When this flag is set to True, the daily forcing data is disaggre-

gated to model timesteps, which involves imposing a diurnal cycle on temperature and radiation and

c Crown Copyright 2014 1

allocating the precipitation to a continuous series of model timesteps within the day. The pressure

P and wind values are unchanged by the disaggregation code and the specific humidity is kept

below the specific humidity at saturation. Any interpolation (specified by interp in JULES DRIVE)

is performed within JULES before the disaggregation code is called. The disaggregation of each

variable is described in more detail in the following sections. The forcing variables required when

l_daily_disagg=T are the same as those needed when l_daily_disagg=F, plus the temperature

range for each grid box for each day.

2.1 Temperature

A diurnal cycle is imposed on the near-surface air temperature using the relation

T = T0 +T

2

cos(2 (t tT

max

) /t

day

), (1)

where T0 and T are the temperature and diurnal temperature ranges before disaggregation

respectively. tday

is the length of a day. tT

max

is the time of day where the temperature is highest

and is calculated with the IMOGEN routine sunny, which assumes that tT

max

occurs 0.15 of a

daylength after local noon i.e.

t

T

max

=

t

up

+ t

down

2

+ 0.15 (t

up

tdown

) (2)

where tup

and tdown

are sunrise and sunset times.

2.2 Specific humidity

The disaggregator code also ensures that the specific humidity q does not go above the specific

humidity at saturation qsat

(T, P ), as calculated by the subroutine qsat i.e.

q = min(q, q

sat

(T, P )). (3)

Note that this condition does not conserve water 1.

2.3 Radiation

The diurnal cycle imposed on the downward longwave radiation is a function of temperature, as

in Huntingford et al. [2010], derived assuming black body radiation and that the diurnal cycle of

temperature is a small perturbation,

R

down

lw

= R

down

lw,0

4

T

T0 3

, (4)

1After the model runs for this report were carried out, an extra option was added which keeps the relative humidityconstant - see the manual for more information.


where Rdownlw,0 is the downward longwave radiation before disaggregation and Rdownlw is the down-

ward longwave radiation after disaggregation.

The downward shortwave radiation is found by

R

down

sw

= R

0,downsw

R

norm

(5)

where Rnorm

is the solar radiation normalisation factor, calculated by the IMOGEN routine sunny,

using the position of the sun in the sky at each timestep for each gridbox. Rdownsw,0 and Rdownsw are the

downward shortwave radiation before and after disaggregation, respectively.

In a general run of JULES, the diffuse radiation can be given explicitly in the forcing data (this is

not the case for IMOGEN runs). A diurnal cycle is imposed on this diffuse radiation using

R

diff

= R

0diff

R

norm

(6)

2.4 Precipitation

The IMOGEN method of disaggregating the precipitation has two components:

The entire days precipitation of each type is first restricted to one event, of duration x

for

x = convective rain, large-scale rain, convective snow and large-scale snow respectively (in

seconds). The start of the precipitation event is distributed randomly throughout the time

period between the beginning of the day (in UTC) and x

before the end of the day.

If the rate of precipitation in a timestep exceeds a maximum precipitation rate (hardwired in as

350 mm/day), then the precipitation is redistributed using the IMOGEN routine redis, which

moves the excess precipitation to dry timesteps immediately before or after the sequence of

wet timesteps, thus lengthening the event beyond x

. This step was included in IMOGEN

because it was found that very high precipitation rates caused numerical issues for the Met

Office Surface Exchange Scheme (MOSES) Essery et al. [2001], which evolved into JULES

(for more details, see code comment in src/imogenday calc.F90).

The mean diurnal cycle produced by this method is not a constant. Since rain events are not

allowed to begin in one day and finish in the subsequent day, the timesteps near the beginning and

end of the day (as defined by the time used internally in JULES, not by local time) will be wet less

often than timesteps in rest of the day. This is illustrated in Figure 1, which shows the mean diurnal

cycle for 100,000 days of precipitation for a range of event durations.

There are four options for precipitation disaggregation in JULES 4.0, specified by the value of

the precip disagg method flag:

1. a constant value of precipitation rate is used for the entire day

2. the IMOGEN method


Figure 1: The diurnal cycle of simulated precipitation rate for 100,000 days, each with a mean dailyprecipitation rate normalised to one. The precipitation for each day has been disaggregated into 72timesteps i.e the timestep length is 20 minutes. The legend shows the precipitation event durationin hours.

3. as for the IMOGEN method except precipitation does not get redistributed when the rate ex-

ceeds max precip rate

4. a proportion x

/t

day

of the timesteps in the day are randomly selected to be wet, and the

precipitation is distributed uniformly among them (tday

is the length of a day in seconds). This

enforces a flat diurnal cycle of precipitation.

Global values of the precipitation event durations (x

) are set by the user (in JULES DRIVE). The

default values are conv rain

= 6 3600 seconds, ls rain

=

ls snow

= 1 3600 seconds. These have

been taken from the defaults in IMOGEN at JULES 3.4.1. For ISI-MIP, conv rain

= 2 3600 seconds,

ls rain

=

ls snow

= 5 3600 seconds, chosen to be physically reasonable. Convective snow was

not given as input in IMOGEN or ISI-MIP. The duration of convective snow events has a default in

JULES 4.0 of conv rain

= 1 3600 seconds.

3 Experimental setup

The performance of this disaggregation method was investigated using output from a global vn8.3

Met Office Unified Model (UM) run in the Global Atmosphere 4.0 (GA4.0) configuration [Walters

et al., 2013] at a model timestep of 20 minutes as forcing data for an offline run of a branch of

JULES 3.4.1 which had the disaggregation code added. This branch was merged with the JULES

trunk before the release of JULES 4.0. The longwave radiation was pre-processed by copying the

data value at the first timestep of each hour into the second and third timesteps of that hour, in order

to eliminate errors that had been introduced by requiring model output at a higher resolution than

the radiation timestep. The JULES namelists for the offline runs were based on the GL4.0 JULES

configuration namelists, edited to be consistent with the UM run, including use of the same ancillary


files.

The results from comparing an offline JULES run driven by this 20 minute forcing data was

compared to the results when JULES was driven by daily means of this forcing data, which are

disaggregated internally using precip disagg method = 3 (i.e. similar to IMOGEN method but with

no maximum precipitation rate being enforced) and the diurnal temperature range and a range of

convective rain event durations conv rain

. For comparison, an additional run was done with three

hourly driving data with interpolation (interp = f).

4 Results

4.1 Diurnal cycle

Fig 2 illustrates the mean diurnal cycle imposed by the disaggregator on the driving data, averaged

over a year of the run (1984), as compared to the diurnal cycle of the model, using the Manaus

FLUXNET site in Brazil as an example (see Appendix for a selection of other FLUXNET sites). As

shown by Eq. 1 and Eq. 2, the disaggregator imposes a sinusoidal diurnal cycle on the temperature,

which in general works reasonably well for the middle of the day but has a minimum earlier than the

original 20 minute driving data; in the example of the Manaus site, this mimimum occurs four hours

too early. The mean short wave diurnal profile is captured well by the disaggregator. However, the

long wave diurnal profile of the original 20 minute forcing data has a much smaller amplitude than

produced by the disaggregator using Eq. 4, and, as was the case for the temperature, the amount

of time between the minimum and the following maximum in the original 20 minute driving data is

less than would be expected if the diurnal cycle followed a sine curve. As stated in Section 1, no

diurnal cycle is imposed on wind or pressure by the disaggregator. The specific humidity diurnal

cycle is also not well matched by the disaggregator.

The convective rainfall produced by the disaggregator at the Manaus site is a good example of

the limitations of the precipitation disaggregation method. Fig 2 shows disaggregated convective

rainfall with a default event duration of 6 hours. In the original 20 minute driving data, there is a clear

diurnal cycle. This is not reproduced at all in the disaggregated convective rainfall, which instead

tends to roughly a trapezoidal pulse, with minimum at midnight UTC, as discussed in Section 2.

Similarly, large-scale rainfall in the original 20 minute driving data has a diurnal cycle which is not

well described by the disaggregator, using the default large-scale rain event duration value of one

hour. If data from more years were included in this plot, the diurnal profile of the disaggregated

large-scale rainfall between 01:00 UTC and 23:00 UTC would tend towards a flat line.

In addition to plotting the mean diurnal cycle, it would also be useful to investigate how well the

variability is captured by the disaggregator. For example, short wave radiation would have variability

introduced by sporadic cloud cover, which is not modelled by the disaggregator.


Figure 2: Diurnal cycle of driving data for the Manaus FLUXNET site, Brazil, averaged over the daysin 1984.


4.2 Global climatology

Figure 3 (top left) shows the total evapotranspiration (total ET) over the full JULES offline run, which

has forcing data at 20 minute resolution created by the UM run (described in Section 3). Figure 3

(bottom right) shows the anomaly from subtracting this result from a run forced with daily means

and no disaggregation. Unsurprisingly, given the complete lack of diurnal cycle in this forcing data,

these anomalies are very large, particularly in the tropics.

Figure 3 (bottom left) shows the anomaly in total evapotranspiration from a run forced with daily

means and disaggregated internally in JULES to the model timestep with respect to the run driven

by the full 20 minute UM output. The event durations have been kept at their default values. It can

be seen that the use of the disaggregator reduces the anomalies drastically, implying that there is

a significant advantage to using the disaggregator if only daily forcing data is available. However,

this should be considered in the context of the anomalies from using 3 hourly forcing data with

interpolation Figure 3 (top right), which are larger than the anomalies from using the disaggregator.

Since 3 hourly forcing data with interpolation represents the model diurnal cycle and variability far

better than this disaggregator can, this implies that the small anomalies in Figure 3 (bottom right)

are perhaps due to a tuning of the disaggregator parameters to partially compensate for model

biases.

There is also some compensation between the contribution of soil ET anomalies and canopy ET

anomalies towards total ET, for example in South East Asia (Figure 7 and Figure 8 in the Appendix).

The anomalies in the soil evapotranspiration in the equatorial regions are predominantly negative in

the run driven by 3 hour means and daily means with no disaggregation, particularly in the tropics,

and the anomalies in the soil evapotranspiration are predominantly positive.

Figure 4 shows the mean runoff in the full run (top left) and the anomalies with the respect to

the full run for the 3 hourly-forced run (top right), the disaggregated run (bottom left) and the daily

forced run without disaggregation (bottom right). Comparison with Figure 3 (bottom left) shows that

the regions with large negative anomalies roughly coincide with the regions where the total evapo-

transpiration anomaly was high, as we would expect since the extra water added to the atmosphere

through evaporation and transpiration is not available for inclusion in the runoff. Figure 4 also shows

that the three hourly forced run and daily run have total runoff anomalies with a higher magnitude

than the disaggregated run, also consistent with the total evapotranspiration results. In some areas,

the contributions to the total runoff anomaly from surface runoff and sub-surface runoff act to com-

pensate for eachother to a certain extent (Figure 9 and Figure 10 in the Appendix), such as in the

region south and east of the Himalayas, where the disaggregated run shows a positive anomaly in

the surface runoff and a negative anomaly in the sub-surface runoff.


18

0

120

60

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120

180

60

3003060

tota

leva

potr

ansp

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0.8

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3.2

4.0

4.8

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/day

18

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120

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3003060

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/day

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3003060

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/day

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/day

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180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=3h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=6h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=12h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=24h

1.5 1.0 0.5 0.0 0.5 1.0 1.5mm/day

total evapotranspiration

Figure 5: Anomalies with respect to the full result, using disaggregation with convective rain eventdurations of 3 hours (top left), 6 hours (top right), 12 hours (bottom left) and 24 hours (bottom right).

4.3 Dependence on the convective rain event duration

Figure 5 shows the anomalies of the disaggregated runs with respect to the full run for convective

rain event duration parameters of conv rain

= 3h, 6h, 12h and 24h. Event duration parameters

for the other types of precipitation are kept at their default values. The total evapotranspiration

increases as conv rain

increases, as we would expect, as larger values of conv rain

means that the

rainfall is less intense and lasts longer.

As the convective rain event duration increases, it is generally the case that soil ET decreases

and canopy ET increases (Figure 11 and Figure 12 in the Appendix), as expected since the lower

event durations give more intense rain, which penetrates the canopy better. In some regions, such

as the Pampas in South America, which is predominantly modelled as C3 grass, soil evaporation

increases as the convective rain event duration increases. For total ET, soil ET and canopy ET, using

the disaggregator with the disaggregation of convective rainfall switched off (i.e. conv rain

= 24h)

is a significant improvement on using no disaggregation at all, illustrating the importance of the

disaggregation of the other driving variables. Out of this selection of values for the convective

rain event duration, conv rain

= 6h results in the lowest anomalies for total evapotranspiration, soil


180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=3h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=6h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=12h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=24h

0.9 0.6 0.3 0.0 0.3 0.6 0.9mm/day

total runoff


evapotranspiration and canopy evapotranspiration in general.

The runoff also depends strongly on the duration of the convective rain events, shown in Figure

6. As for evaporation, conv rain

= 6h appears to perform the best for total runoff out of the values

of conv rain

illustrated here. As the duration decreases (and so the rainfall intensity increases),

the surface runoff increases and sub-surface runoff decreases (Figure 13 and Figure 14 in the

Appendix), as we would expect. In some regions, such as the Andes and the region south and east

of the Himalayas for conv rain

= 3h, the negative anomaly in sub-surface runoff roughly mirrors the

positive surface runoff anomaly. In other regions, such as the Congo basin for conv rain

= 12h, there

is a large negative anomaly in surface runoff, but no correspondingly large anomaly in sub-surface

runoff, leading to a large negative anomaly in the total runoff, consistent with the large positive

anomaly in total evapotranspiration, which we saw in Figure 5 (top right).

In summary, in it generally the case that increasing the convective rain event duration parameter

results in more of the rainfall being evaporated and less contributing to the runoff, particularly in

equatorial regions. Evaporation and runoff are very sensitive to this parameter, which should be set

with care. From the results shown here, using conv rain

= 6h appears to achieve a good compro-


mise for most areas of the world. However, selecting the values of the event durations by tuning the

evaporation and runoff anomalies could hide biases in the climatology of the model. There is good

indication that this is happening here, since, as we saw in Section 4.2, using conv rain

= 6h can

result in lower anomalies than using 3 hourly forcing data with interpolation.

5 Summary

A disaggregation option has been built in to JULES 4.0, which will allow offline runs to be forced

by daily meteorological data, which is then disaggregated to the model timestep internally using the

IMOGEN method. This option will be particularly useful for model intercomparisons, where subdaily

forcing data is not always available. We describe the implementation of this method in detail and

show an example of the resulting mean diurnal cycle of forcing variables after disaggregation.

We used UM output to force a set of offline JULES runs, to compare forcing at 20 minute res-

olution with forcing at three hourly resolution with interpolation and daily forcing with and without

disaggregation. There was a clear advantage to using the disaggregator when using daily driving

data. However, the disaggregated run with default parameters also reproduced the mean climate in

some cases better than the 3-hourly forced run, which implies that these parameter values may be

partially compensating for biases in the model. Since the results are sensitive to the disaggregation

parameters, we would encourage the user to consider carefully which values are most appropriate

for their individual needs and possibly to perform some initial analysis to assess the impact of this

choice on the region, output variables and timescales they are most interested in.

An interesting next step would be to investigate the variability of the model output produced using

the disaggregation option. It would also be useful to see how the disaggregator performs compared

to high temporal resolution observational forcing data since, for example, there are known issues

with the model diurnal cycle of precipitation [Stephens et al., 2010, Stratton and Stirling, 2012,

Stirling and Stratton, 2012].

There are many ways in which this disaggregator could be improved, which we hope will be

taken forward by the JULES community. For example, one simple extension would be to allow

the precipitation event duration parameters to vary spatially, although the simplistic nature of the

precipitation model (all the rainfall in one event, with constant intensity) means that it is not clear how

the duration parameter could be derived from observed precipitation, even when these observations

are available at sufficiently high temporal resolution. Other interesting possibilities would be to

distribute the daily precipitation amongst the timesteps in that day using a cascade process, or

allow the user to specify a normalised diurnal profile for each gridbox.


6 Acknowledgements

Many thanks to Pete Falloon, Martin Best and Andy Wiltshire for reading through various stages of

the draft and their helpful advice and suggestions. We are also very grateful to Spencer Liddicoat,

Camilla Mathison and James Manners for their assistance with the Unified Model set-up.

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http://dx.doi.org/10.1175/2011jhm1369.1.

A Additional plots: global climatology plots and diurnal cycles

of forcing variables at FLUXNET sites


18

0

120

60

060

120

180

60

3003060

soil

evap

otra

nspi

rati

on

0.0

0.6

1.2

1.8

2.4

3.0

3.6

4.2

mm

/day

18

0

120

60

060

120

180

60

3003060

soil

evap

otra

nspi

rati

onan

omal

y,3h

rly,

inte

rpol

ated

0.

9

0.6

0.

30.

00.

30.

60.

9

mm

/day

18

0

120

60

060

120

180

60

3003060

soil

evap

otra

nspi

rati

onan

omal

y,di

sagg

,conv

rain

=6h

0.

9

0.6

0.

30.

00.

30.

60.

9

mm

/day

18

0

120

60

060

120

180

60

3003060

soil

evap

otra

nspi

rati

onan

omal

y,da

ily,n

odi

sagg

0.

9

0.6

0.

30.

00.

30.

60.

9

mm

/day

Figu

re7:

Top

left:

Out

putv

aria

ble

from

JULE

Sru

nw

ithfu

ll20

min

ute

reso

lutio

nfo

rcin

g.To

prig

ht:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

g3

hour

lyfo

rcin

gw

ithin

terp

olat

ion.

Bot

tom

left:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

gdi

sagg

rega

tion

with

defa

ulte

vent

dura

tions

.B

otto

mrig

ht:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

gda

ilyfo

rcin

gw

ithno

inte

rpol

atio

n.


18

0

120

60

060

120

180

60

3003060

evap

from

cano

py/s

urfa

cest

ore

0.0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

mm

/day

18

0

120

60

060

120

180

60

3003060

evap

from

cano

py/s

urfa

cest

ore

anom

aly,

3hrl

y,in

terp

olat

ed

1.

8

1.2

0.

60.

00.

61.

21.

8

mm

/day

18

0

120

60

060

120

180

60

3003060

evap

from

cano

py/s

urfa

cest

ore

anom

aly,

disa

gg,

conv

rain

=6h

1.

8

1.2

0.

60.

00.

61.

21.

8

mm

/day

18

0

120

60

060

120

180

60

3003060

evap

from

cano

py/s

urfa

cest

ore

anom

aly,

daily

,no

disa

gg

1.

8

1.2

0.

60.

00.

61.

21.

8

mm

/day

Figu

re8:

Top

left:

Out

putv

aria

ble

from

JULE

Sru

nw

ithfu

ll20

min

ute

reso

lutio

nfo

rcin

g.To

prig

ht:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

g3

hour

lyfo

rcin

gw

ithin

terp

olat

ion.

Bot

tom

left:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

gdi

sagg

rega

tion

with

defa

ulte

vent

dura

tions

.B

otto

mrig

ht:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

gda

ilyfo

rcin

gw

ithno

inte

rpol

atio

n.


18

0

120

60

060

120

180

60

3003060

surf

ace

runo

ff

0.0

0.8

1.6

2.4

3.2

4.0

4.8

mm

/day

18

0

120

60

060

120

180

60

3003060

surf

ace

runo

ffan

omal

y,3h

rly,

inte

rpol

ated

0.

9

0.6

0.

30.

00.

30.

60.

9

mm

/day

18

0

120

60

060

120

180

60

3003060

surf

ace

runo

ffan

omal

y,di

sagg

,conv

rain

=6h

0.

9

0.6

0.

30.

00.

30.

60.

9

mm

/day

18

0

120

60

060

120

180

60

3003060

surf

ace

runo

ffan

omal

y,da

ily,n

odi

sagg

0.

9

0.6

0.

30.

00.

30.

60.

9

mm

/day

Figu

re9:

Top

left:

Out

putv

aria

ble

from

JULE

Sru

nw

ithfu

ll20

min

ute

reso

lutio

nfo

rcin

g.To

prig

ht:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

g3

hour

lyfo

rcin

gw

ithin

terp

olat

ion.

Bot

tom

left:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

gdi

sagg

rega

tion

with

defa

ulte

vent

dura

tions

.B

otto

mrig

ht:

Ano

mal

ies

with

resp

ectt

oth

efu

llre

sult,

usin

gda

ilyfo

rcin

gw

ithno

inte

rpol

atio

n.


18

0

120

60

060

120

180

60

3003060

sub

surf

ace

runo

ff

0.0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

mm

/day

18

0

120

60

060

120

180

60

3003060

sub

surf

ace

runo

ffan

omal

y,3h

rly,

inte

rpol

ated

0.

48

0.32

0.

160.

000.

160.

320.

48

mm

/day

18

0

120

60

060

120

180

60

3003060

sub

surf

ace

runo

ffan

omal

y,di

sagg

,conv

rain

=6h

0.

48

0.32

0.

160.

000.

160.

320.

48

mm

/day

18

0

120

60

060

120

180

60

3003060

sub

surf

ace

runo

ffan

omal

y,da

ily,n

odi

sagg

0.

48

0.32

0.

160.

000.

160.

320.

48

mm

/day

Figu

re10

:Top

left:

Out

putv

aria

ble

from

JULE

Sru

nw

ithfu

ll20

min

ute

reso

lutio

nfo

rcin

g.To

prig

ht:A

nom

alie

sw

ithre

spec

tto

the

full

resu

lt,us

ing

3ho

urly

forc

ing

with

inte

rpol

atio

n.B

otto

mle

ft:A

nom

alie

sw

ithre

spec

tto

the

full

resu

lt,us

ing

disa

ggre

gatio

nw

ithde

faul

teve

ntdu

ratio

ns.

Bot

tom

right

:A

nom

alie

sw

ithre

spec

tto

the

full

resu

lt,us

ing

daily

forc

ing

with

noin

terp

olat

ion.


180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=3h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=6h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=12h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=24h

0.9 0.6 0.3 0.0 0.3 0.6 0.9mm/day

soil evapotranspiration


180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=3h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=6h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=12h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=24h

1.8 1.2 0.6 0.0 0.6 1.2 1.8mm/day

evap from canopy/surface store



180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=3h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=6h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=12h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=24h

0.9 0.6 0.3 0.0 0.3 0.6 0.9mm/day

surface runoff


180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=3h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=6h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=12h

180 120 60 0 60 120 180

60

30

0

30

60

disagg, conv rain

=24h

0.48 0.32 0.16 0.00 0.16 0.32 0.48mm/day

sub surface runoff



Figure 15: Diurnal cycle of driving data for the Hyytiala FLUXNET site, Finland, averaged over thedays in 1984.


Figure 16: Diurnal cycle of driving data for the Vielsalm FLUXNET site, Belgium, averaged over thedays in 1984.


Figure 17: Diurnal cycle of driving data for the Collelongo FLUXNET site, Italy, averaged over thedays in 1984.


Figure 18: Diurnal cycle of driving data for the Harvard Forest FLUXNET site, Massachusetts,averaged over the days in 1984.


Figure 19: Diurnal cycle of driving data for the Bondville FLUXNET site, Illinois, averaged over thedays in 1984.


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